The problem of data sparsity has long been a challenge in recommendation systems, and previous studies have attempted to address this issue by incorporating side information. However, this approach often introduces side effects such as noise, availability issues, and low data quality, which in turn hinder the accurate modeling of user preferences and adversely impact recommendation performance. In light of the recent advancements in large language models (LLMs), which possess extensive knowledge bases and strong reasoning capabilities, we propose a novel framework called LLMRec that enhances recommender systems by employing three simple yet effective LLM-based graph augmentation strategies. Our approach leverages the rich content available within online platforms (e.g., Netflix, MovieLens) to augment the interaction graph in three ways: (i) reinforcing user-item interaction egde, (ii) enhancing the understanding of item node attributes, and (iii) conducting user node profiling, intuitively from the natural language perspective. By employing these strategies, we address the challenges posed by sparse implicit feedback and low-quality side information in recommenders. Besides, to ensure the quality of the augmentation, we develop a denoised data robustification mechanism that includes techniques of noisy implicit feedback pruning and MAE-based feature enhancement that help refine the augmented data and improve its reliability. Furthermore, we provide theoretical analysis to support the effectiveness of LLMRec and clarify the benefits of our method in facilitating model optimization. Experimental results on benchmark datasets demonstrate the superiority of our LLM-based augmentation approach over state-of-the-art techniques. To ensure reproducibility, we have made our code and augmented data publicly available at: https://github.com/HKUDS/LLMRec.git

翻译：数据稀疏问题长期以来一直是推荐系统中的挑战，先前的研究尝试通过引入侧信息来解决该问题。然而，这种方法常引入噪声、可用性问题和低数据质量等副作用，进而阻碍对用户偏好的精确建模，并对推荐性能产生不利影响。鉴于大语言模型（LLM）的最新进展——其拥有广泛的知识库和强大的推理能力，我们提出了一种名为LLMRec的新框架，通过采用三种简单而有效的基于LLM的图增强策略来提升推荐系统性能。我们的方法利用在线平台（如Netflix、MovieLens）中丰富的可用内容，从自然语言视角以三种方式增强交互图：（i）强化用户-项目交互边，（ii）增强对项目节点属性的理解，以及（iii）进行用户节点画像。通过采用这些策略，我们解决了推荐系统中稀疏隐式反馈和低质量侧信息带来的挑战。此外，为确保增强质量，我们开发了一种去噪数据鲁棒化机制，包含噪声隐式反馈剪枝和基于MAE的特征增强技术，有助于精炼增强数据并提升其可靠性。同时，我们提供了理论分析来支持LLMRec的有效性，并阐明我们的方法在促进模型优化方面的优势。在基准数据集上的实验结果表明，我们基于LLM的增强方法优于现有最先进技术。为确保可复现性，我们已在https://github.com/HKUDS/LLMRec.git 公开提供代码和增强数据。